# Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN

arXiv: https://arxiv.org/abs/2208.04229 · HTML: https://mlsanity.com/paper/2208.04229/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Network-Aware Optimization of Distributed Learning for Fog Computing](https://mlsanity.com/paper/2004.08488/) (2020)
- [Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G](https://mlsanity.com/paper/1907.07862/) (2019)
- [Reinforcement Learning Based Dynamic Function Splitting in Disaggregated Green Open RANs](https://mlsanity.com/paper/2012.03213/) (2020)
- [Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks](https://mlsanity.com/paper/1808.07647/) (2018)
- [Network Support for High-performance Distributed Machine Learning](https://mlsanity.com/paper/2102.03394/) (2021)
- [Computation Offloading for IoT in C-RAN: Optimization and Deep Learning](https://mlsanity.com/paper/1909.10696/) (2019)
- [CellOS: Zero-touch Softwarized Open Cellular Networks](https://mlsanity.com/paper/2004.03418/) (2020)

## Abstract (arXiv)

Open Radio Access Network (O-RAN) is an emerging paradigm, whereby virtualized network infrastructure elements from different vendors communicate via open, standardized interfaces. A key element therein is the RAN Intelligent Controller (RIC), an Artificial Intelligence (AI)-based controller. Traditionally, all data available in the network has been used to train a single AI model to be used at the RIC. This paper introduces, discusses, and evaluates the creation of multiple AI model instances at different RICs, leveraging information from some (or all) locations for their training. This brings about a flexible relationship between gNBs, the AI models used to control them, and the data such models are trained with. Experiments with real-world traces show how using multiple AI model instances that choose training data from specific locations improve the performance of traditional approaches following the hoarding strategy.